metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vet-sm
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6643894107600341
vet-sm
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0088
- Accuracy: 0.6644
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4639 | 1.0 | 207 | 1.4477 | 0.4765 |
| 1.1673 | 2.0 | 415 | 1.1658 | 0.5824 |
| 0.875 | 3.0 | 622 | 1.0805 | 0.6157 |
| 0.5449 | 4.0 | 830 | 1.0399 | 0.6413 |
| 0.4905 | 4.99 | 1035 | 1.0088 | 0.6644 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1